Investigating Data Collected from W’s Laptop.
W’s professional work mostly involves face-to-face interaction with clients and not much computer use. However, W says that often he will use the computer to keep-up-to date with emails, the news, and current events. W’s work requires him to occasionally use social media and to read trending news stories.
As P often uses the computer for his work and W uses it occasionally we cooperatively decided it might be beneficial for them to track their productivity when using the computer. This was because both participants said that they spend a lot of their time using their computer, both for work and recreation, and not tracking data from this central part of their day would be an oversight.
For this project, we decided to run a week-long productivity experiment. Where-by we would track all activities done on the computers and these activities would be categorised into their nature and how ‘productive’ this was. This allowed for specific activities to remain concealed, but for the nature of the activity to be revealed in the wider picture. Further, it was also agreed with the participants that daily quantified break-downs of productivity would not be revealed in my project so not to endanger their employment.
However, it’s not just numbers that should be used to provide insight, but contextual correlation plays a huge role when it comes to reaching conclusions. This quantitive data is complemented with ethnographic qualitative data generated through conversation with W.
W told me that this week was especially difficult for him for many reasons. From Monday to Thursday he was working especially hard within the office, whilst on Tuesday he spent most of the day working on a project he didn’t particularly enjoy. After work W spent most days in and watched TV through his computer. On Thursday, however, he went out to celebrate a friend’s birthday which is why we see ‘non-productive’ activities that day. On Friday, W took the day off work, and said he was very lazy that day as he was “recovering from the night before”. This ‘lazy day is why we see over 8 hours of unproductive computer usage that day.
Similarly to Thursday and Friday, W went out on Saturday and relaxed on Sunday. However, he spent some time catching up on his work from the past few days he had put off.
W told me that he expected Tuesday to be his most productive day, even though it was the least enjoyable. This was because he spent most of his day working on a specific project at the computer. This assumption was correct, however he only spent around 3hours of productive time that day. In W’s case, it is important that we take into account that much of his work is away from his computer. And so, this doesn’t mean he was only productive for 3 hours that day.
W spoke to me about his usual work day. He told me that usually in the mornings he is quite sluggish and uses his computer to catch up on emails and the news. This narrative was consistent with the data shown. Further, W told me that he often then has a lot of interaction with clients and work colleagues in the mid-morning tim afternoon. This didn’t seem to correlate fully with the data. However, when questioned on this W told me that often, between meetings and interactions he browse the internet. This explanation describes the peaks of news/opinion sites and reference tools seen in the early afternoon.
W said he has a few late nights during the week, and often watches TV through his laptop before sleeping. Again, this was shown in the data, especially at 12 o’clock. It is also interesting to note that some productive work was completed throughout the evening, peaking between 8pm and 10pm. W did not mention this, nor seem to recall any specific events which led him to work at night.